ArticleBMC molecular and cell biology2019
Metabolic profiles to predict long-term cancer and mortality: the use of latent class analysis.
Article in BMC molecular and cell biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Article
- Article
- Latent class analysis of placental histopathology: a novel approach to classifying early and late preterm births.American journal of obstetrics and gynecology · 2022Article
- Association between serum markers of the humoral immune system and inflammation in the Swedish AMORIS study.BMC immunology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetabolites are genetically and environmentally determined. Consequently, they can be used to characterize environmental exposures and reveal biochemical mechanisms that link exposure to disease. To explore disease susceptibility and improve population risk stratification, we aimed to identify metabolic profiles linked to carcinogenesis and mortality and their intrinsic associations by characterizing subgroups of individuals based on serum biomarker measurements. We included 13,615 participants from the Swedish Apolipoprotein MOrtality RISk Study who had measurements for 19 biomarkers representative of central metabolic pathways. Latent Class Analysis (LCA) was applied to characterise individuals based on their biomarker values (according to medical cut-offs), which were then examined as predictors of cancer and death using multivariable Cox proportional hazards models.
resultsLCA identified four metabolic profiles within the population: (1) normal values for all markers (63% of population); (2) abnormal values for lipids (22%); (3) abnormal values for liver functioning (9%); (4) abnormal values for iron and inflammation metabolism (6%). All metabolic profiles (classes 2-4) increased risk of cancer and mortality, compared to class 1 (e.g. HR for overall death was 1.26 (95% CI: 1.16-1.37), 1.67 (95% CI: 1.47-1.90), and 1.21 (95% CI: 1.05-1.41) for class 2, 3, and 4, respectively).
conclusionWe present an innovative approach to risk stratify a well-defined population based on LCA metabolic-defined subgroups for cancer and mortality. Our results indicate that standard of care baseline serum markers, when assembled into meaningful metabolic profiles, could help assess long term risk of disease and provide insight in disease susceptibility and etiology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.